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Date: April 15, 2026 Time: 1:30 P.M. - 2:30 P.M. Location: Morrison & Foerster, 250 W 55th St (map) Conference Agenda: **https://www.proximatecommunity.org/summit** Format: Interactive workshop Attendees: 30-35 Presenters: Clayton Bryan and Joyce Koltisko

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Background

This session explores what changes when AI-native companies can reach meaningful scale with far less capital, smaller teams, and stronger unit economics than the traditional venture model assumes.

**Clayton Bryan,** formerly Partner and now Advisor at 500 Global, will present the core thesis: some of the strongest AI-native companies are scaling quickly while remaining unusually lean, which raises hard questions about round size, ownership, governance, and whether traditional venture still fits the best businesses in the category.

Joyce Koltisko, Principal at the Yale Investments Office will help surface the LP implications: how allocators should think about manager selection, fund construction, returns, and what it means when high-quality companies may need less institutional capital to reach maturity.

Jorge Torres will moderate the session.

Run-of-Session

• 00:00–00:15 — Thesis and tension: Clayton presents the argument, the proof cases, and the market data. Joyce responds with the LP view: if the underlying companies are changing, what follows for fund economics, liquidity expectations, and manager evaluation?
• 00:15–00:30 — What changes in venture practice?: Where the traditional VC model still works, where it is under pressure, and which parts of the playbook may need to evolve (round construction, pricing, board expectations, ownership targets, value-add).

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**Question Bank

Clayton**

1. How do you distinguish between a company that's capital-efficient by design and one that's capital-efficient because it can't raise?
2. What's the failure mode of capital-efficient AI companies? If they don't need much money, what kills them?
3. You evaluated 50,000+ startups at 500 Global — what percentage of AI companies you saw fit this capital-efficient pattern vs. just claiming to?
4. If these companies don't need venture capital, why would they take it at all? What's the investor's value-add when the check size is small?
5. Doesn't capital efficiency just mean smaller outcomes? If a company can get to profitability on $5M, isn't the ceiling lower too?
6. How do you think about defensibility in capital-efficient AI? If it's cheap to build, isn't it cheap for a competitor to replicate?
7. What are the sectors or business models where the capital-efficient AI thesis doesn't apply?
8. What happens to these companies when a well-funded competitor enters the market? Is capital efficiency an advantage or a vulnerability at scale?
9. Do you think this is a permanent structural shift, or are we in a window that closes once AI tooling commoditizes and competition increases?

Joyce

1. If the best AI companies need less capital, what happens to fund sizes? Should emerging managers be raising smaller funds? How can you operate the firm as a business with smaller management fees?
2. How do you evaluate a fund manager whose strategy is built around capital-efficient AI? Will LPs need new metrics to underwrite such strategies?
3. Does DPI timing change when portfolio companies can reach profitability faster?
4. As an LP, are you worried that capital-efficient companies will produce solid returns but not the 10x+ outcomes that make venture math work?
5. How should LPs think about manager selection when the playbook is changing this fast? What signals tell you a GP understands this shift vs. just using it as a fundraising narrative?
6. If companies don't need priced rounds, how do LPs get markups? Does the J-curve just disappear, and is that good or bad?
7. What's your view on GPs who invest through SAFEs and revenue-based instruments rather than priced equity? Does that make fund reporting harder for you?
8. Are institutional LPs structurally set up to invest in $20–50M funds, or does the operational overhead make small funds unattractive regardless of returns? In other words, can a fund dedicated to working with the founders of capital efficient AI companies be too small to absorb an institutional check?
9. How do you think about concentration risk when a fund's best companies may never need follow-on capital — do you want the GP to redeploy reserves into new deals? </aside>

• 00:30–00:50 — Hot Takes Exercise: Jorge will read a series of provocative statements and participants physically move to one side of the room (agree) or the other (disagree). After each statement, the moderator pulls 2–3 people from each side to defend their position, then Clayton and Joyce weigh in.

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Hot Takes

1. The most valued private company of 2030 will have fewer than 8 employees.
2. Board seats create more problems than they solve for capital-efficient companies.
3. Ownership targets of 20%+ are a relic of the software era and will look absurd within 2 years.
4. Revenue-based financing will replace equity rounds for the majority of AI-native businesses within a decade.
5. The traditional VC associate — sourcing deals, writing memos, building models — will be fully automated by 2028.
6. Within a decade, the top 5 names on the Forbes Midas List will be solo GPs with AI tools.
7. Most venture-backed AI companies would have been better off bootstrapping.
8. Founders who turn down term sheets to stay capital-efficient will outperform founders who raise at every opportunity.
9. If your AI startup needs more than $10M to reach profitability, your business model is broken. </aside>

• 00:50–01:00 — Q&A and Closing

Artifact (optional)

• Takeaway: a slide deck, short one-pager with the core thesis, proof points, and a simple framework for evaluating whether a company or fund strategy fits the capital-efficient AI pattern (QR code).